An explorative study for leveraging transcriptomic data of embryonic stem cells in mining cancer sternness genes, regulators, and networks

Due to the exquisite ability of cancer sternness to facilitate tumor initiation, metastasis, and cancer therapy resistance, targeting cancer sternness is expected to have clinical implications for cancer treatment. Genes are fundamental for forming and maintaining sternness. Considering shared genetic programs and pathways between embryonic stem cells and cancer stem cells, we conducted a study analyzing transcriptomic data of embryonic stem cells for mining potential cancer sternness genes. Firstly, we integrated co-expression and regression models and predicted 820 sternness genes. Results of gene enrichment analysis confirmed the good prediction performance for enriched signatures in cancer stem cells. Secondly, we provided an application case using the predicted sternness genes to construct a breast cancer sternness network. Mining on the network identified CD44, SOX2, TWIST1, and DLG4 as potential regulators of breast cancer sternness. Thirdly, using the signature of 31,028 chemical perturbations and their correlation with sternness marker genes, we predicted 67 sternness inhibitors with reasonable accuracy of 78%. Two drugs, namely Rigosertib and Proscillaridin A, were first identified as potential sternness inhibitors for melanoma and colon cancer, respectively. Overall, mining embryonic stem cell data provides a valuable way to identify cancer sternness regulators.

Automated summary

In this study, using transcriptomic data from embryonic stem cells, potential cancer stemness genes were identified, and a breast cancer stemness network was constructed. Furthermore, by analyzing the correlation between chemical perturbations and stemness marker genes, several drugs with potential inhibitory effects on stemness were predicted.